SuperNeRF: High-Precision 3-D Reconstruction for Large-Scale Scenes

Recent approaches based on neural radiance field (NeRF) showcase remarkable results in the 3-D reconstruction of small-scale scenes by encoding volume density and color observations using implicit functions. However, when confronted with complex and diverse large-scale scenes, it invariably experiences issues such as blurry textures and missing details. In this work, we present a superpixel-based neural radiance field (named SuperNeRF), an additional loss for learning radiance fields that takes advantage of superpixels texture constraints. Building upon NeRF, we leverage superpixels to establish spatial consistency constraints, enabling the precise extraction of 3-D geometry and appearance for large-scale scenes. SuperNeRF is capable of guiding locally adjacent and similar pixels to form nearly consistent ray termination distributions, and it is compatible with the state-of-the-art NeRF-based methods. Comprehensive experiments conducted on representative aviation and aerospace datasets demonstrate that our SuperNeRF exhibits a significant superiority in accuracy over state-of-the-art methods. Code will be available at https://github.com/xczbecalm/supernerf.